PASH Analysis

1. Description of study population

There are 129 visits with corresponding epigenetic ages available among 106 female subjects. For these 106 subjects, 83 have been visited once and 23 have been visited twice.

The following tables summarize all the information of the first visit of these 106 subjects.

Baseline characteristics (confounders)

Characteristic	N = 1291
Age	58 (15)
county
Fuyuan	61 / 119 (51%)
Xuanwe	58 / 119 (49%)
(Missing)	10
BMI	22.0 (3.5)
ses
0	65 / 119 (55%)
1	54 / 119 (45%)
(Missing)	10
edu
1	81 / 119 (68%)
2	19 / 119 (16%)
3	14 / 119 (12%)
4	5 / 119 (4.2%)
(Missing)	10
1 Mean (SD); n / N (%)

Epigenetic ages

Characteristic	N = 1291
DNAmAge	58 (13)
DNAmAgeHannum	60 (14)
DNAmPhenoAge	56 (14)
DNAmAgeSkinBloodClock	57 (13)
DNAmGrimAge	56 (11)
DNAmTL	6.82 (0.32)
1 Mean (SD)

Epigenetic ages accelarations

Characteristic	N = 1291
AgeAccelerationResidual	0.4 (4.7)
AgeAccelerationResidualHannum	-0.2 (4.0)
AgeAccelPheno	-0.3 (4.3)
DNAmAgeSkinBloodClockAdjAge	0.2 (3.4)
AgeAccelGrim	-0.43 (2.99)
DNAmTLAdjAge	0.03 (0.18)
IEAA	0.2 (4.3)
EEAA	-0.3 (5.1)
1 Mean (SD)

Fuel/stove type exposures

Characteristic	N = 1291
curFuel
Smokeles	17 / 112 (15%)
Smoky	87 / 112 (78%)
Wood_and_or_Plant	8 / 112 (7.1%)
(Missing)	17
brthFuel
Mix	53 / 116 (46%)
Smokeles	5 / 116 (4.3%)
Smoky	47 / 116 (41%)
Wood	11 / 116 (9.5%)
(Missing)	13
cumFuel
Mix	82 / 119 (69%)
Smoky	37 / 119 (31%)
(Missing)	10
curStove
Firepit_and_unventilated	22 / 112 (20%)
Mix	19 / 112 (17%)
Portable_stove	20 / 112 (18%)
Ventilated	51 / 112 (46%)
(Missing)	17
1 n / N (%)

5MC exposures

Characteristic	N = 1291
cur_5mc	8.2 (4.3)
(Missing)	13
cum_5mc	278 (153)
(Missing)	13
bir_5mc	5.30 (2.83)
(Missing)	13
cur_5mc_measured	13 (39)
(Missing)	81
1 Mean (SD)

Cluster-based exposures

clusCUR6

Clusters based on model-based exposure estimates at or shortly before the visit

Characteristic	N = 1291
CUR6_BC_PAH6	0.21 (0.98)
(Missing)	13
CUR6_PAH31	0.20 (0.92)
(Missing)	13
CUR6_NkF	-0.11 (1.10)
(Missing)	13
CUR6_PM_RET	-0.01 (0.93)
(Missing)	13
CUR6_NO2	0.19 (0.99)
(Missing)	13
CUR6_SO2	-0.13 (0.89)
(Missing)	13
1 Mean (SD)

clusCHLD5

Clusters based on model-based exposure estimates accrued before age 18

Characteristic	N = 1291
CHLD5_X7	-0.02 (0.92)
(Missing)	13
CHLD5_X33	0.18 (0.98)
(Missing)	13
CHLD5_NkF	-0.11 (1.13)
(Missing)	13
CHLD5_NO2	0.23 (1.09)
(Missing)	13
CHLD5_SO2	0.02 (0.88)
(Missing)	13
1 Mean (SD)

clusCUM6

Clusters based on model-based lifetime exposure estimates

Characteristic	N = 1291
CUM6_BC_NO2_PM	0.11 (1.01)
(Missing)	13
CUM6_PAH36	0.21 (0.97)
(Missing)	13
CUM6_DlP	-0.20 (1.04)
(Missing)	13
CUM6_NkF	-0.04 (1.11)
(Missing)	13
CUM6_RET	-0.09 (0.97)
(Missing)	13
CUM6_SO2	-0.07 (0.93)
(Missing)	13
1 Mean (SD)

clusMEAS6

Clusters based on pollutant measurements

Characteristic	N = 1291
MEAS6_BC_PM_RET	0.04 (0.93)
(Missing)	85
MEAS6_X31	0.06 (1.05)
(Missing)	85
MEAS6_X5	0.01 (1.00)
(Missing)	85
MEAS6_DlP	0.02 (1.00)
(Missing)	85
MEAS6_NkF	0.08 (1.04)
(Missing)	85
MEAS6_NO2_SO2	-0.03 (0.99)
(Missing)	85
1 Mean (SD)

clusURI5

Clusters based on urinary biomarkers

Characteristic	N = 1291
URI5_NAP_1M_2M	0.01 (0.96)
(Missing)	25
URI5_ACE	-0.06 (0.99)
(Missing)	25
URI5_FLU_PHE	-0.05 (0.95)
(Missing)	25
URI5_PYR	-0.02 (0.91)
(Missing)	25
URI5_CHR	-0.01 (1.01)
(Missing)	25
1 Mean (SD)

Ambient exposures

Characteristic	N = 1291
bap_air	65 (87)
(Missing)	5
pm25_air	197 (176)
ANY_air	1,038 (1,792)
(Missing)	41
BPE_air	71 (92)
(Missing)	5
BaA_air	88 (145)
(Missing)	5
BbF_air	106 (143)
(Missing)	5
BkF_air	23 (32)
(Missing)	5
CHR_air	83 (133)
(Missing)	5
DBA_air	24 (36)
(Missing)	5
FLT_air	60 (136)
(Missing)	5
FLU_air	486 (680)
(Missing)	41
IPY_air	43 (51)
(Missing)	5
NAP_air	5,731 (8,737)
(Missing)	41
PHE_air	730 (1,054)
(Missing)	41
PYR_air	66 (139)
(Missing)	5
1 Mean (SD)

Urinary biomarkers

Characteristic	N = 1291
Benzanthracene_Chrysene_urine	0.92 (3.17)
(Missing)	2
Naphthalene_urine	224 (686)
Methylnaphthalene_2_urine	46 (59)
(Missing)	9
Methylnaphthalene_1_urine	20 (24)
(Missing)	4
Acenaphthene_urine	7 (11)
Phenanthrene_Anthracene_urine	204 (275)
Fluoranthene_urine	21 (23)
Pyrene_urine	0.71 (0.57)
(Missing)	18
1 Mean (SD)

2. Self-reported categorical fuel type

a. Primary analysis

i. Current fuel type

Average EAA difference of current fuel type, smokeless/wood or plant compared to smoky coal-significant association for GrimAge (strong prior given the primary molecular signature in lung tumor samples in this population is almost identical to tobacco smoking molecular signature)

The numbers of observations with each current fuel type:

## 
##          Smokeles             Smoky Wood_and_or_Plant 
##                17                87                 8

In this section, we perform the generalized estimating equations (GEE) to evaluate the associations between current fuel type and each Epigenetic Age Acceleration with the formula: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * I(\text{Smokeles}) + \beta_2 * I(\text{Wood_and_or_Plant}) \\ & + \beta_3 * county + \beta_4 * BMI + \beta_5 * ses + \beta_6 * edu + \epsilon \end{aligned} \]

Results:

## The estimated average GrimAge EAA difference of current fuel type, smokeless/wood or plant compared to smoky coal:

##                             coefficient       std   ci_lower   ci_upper
## Smoky (reference/intercept)   4.5873328 1.9055373  0.8524796  8.3221859
## Smokeles                     -1.7863625 0.7216448 -3.2007862 -0.3719388
## Wood_and_or_Plant             0.6039894 1.5781848 -2.4892529  3.6972317
##                                  p_val sig_level
## Smoky (reference/intercept) 0.01606790   <= 0.05
## Smokeles                    0.01330854   <= 0.05
## Wood_and_or_Plant           0.70193373    > 0.05

b. Secondary analyses

Additional analyses to present in tandem with the primary analysis findings given that cumulative and long-term exposure may impact methylation

i. Cumulative fuel type

Average EAA difference of cumulative fuel type, mixed fuel compared to smoky coal-significant association for GrimAge

The numbers of observations with each current fuel type:

## 
##   Mix Smoky 
##    82    37

In this section, we perform the generalized estimating equations (GEE) to evaluate the associations between cumulative fuel type and each Epigenetic Age Acceleration with the formula: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * I(\text{Mix}) \\ & + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \epsilon \end{aligned} \]

Results:

## The estimated average GrimAge EAA difference of cumulative fuel type, mix compared to smoky coal:

##                             coefficient       std  ci_lower   ci_upper
## Smoky (reference/intercept)    4.254226 1.7709125  0.783238 7.72521486
## Mix                           -1.016389 0.5313504 -2.057836 0.02505759
##                                  p_val sig_level
## Smoky (reference/intercept) 0.01629326   <= 0.05
## Mix                         0.05576832    > 0.05

ii. Childhood fuel type

iAverage EAA difference for childhood fuel type, smokeless/wood/mixed fuel compared to smoky-significant association for GrimAge

The numbers of observations with each childhood fuel type:

## 
##      Mix Smokeles    Smoky     Wood 
##       53        5       47       11

In this section, we perform the generalized estimating equations (GEE) to evaluate the association between current fuel type and the Grim Epigenetic Age Acceleration with the formula: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * I(\text{Wood}) + \beta_2 * I(\text{Smokeles}) + \beta_3 * I(\text{Mix}) \\ & + \beta_4 * county + \beta_5 * BMI + \beta_6 * ses + \beta_7 * edu + \epsilon \end{aligned} \]

Results:

## The estimated average GrimAge EAA difference of childhood fuel type, smokeless/wood/mixed compared to smoky coal:

##                             coefficient       std   ci_lower   ci_upper
## Smoky (reference/intercept)   4.0266496 1.7471820  0.6021728  7.4511264
## Wood                          0.1554411 0.8731262 -1.5558862  1.8667684
## Smokeles                     -3.8872162 1.1879066 -6.2155132 -1.5589192
## Mix                          -1.4618057 0.5666144 -2.5723699 -0.3512415
##                                  p_val sig_level
## Smoky (reference/intercept) 0.02118597   <= 0.05
## Wood                        0.85870082    > 0.05
## Smokeles                    0.00106667   <= 0.01
## Mix                         0.00988303   <= 0.01

3. Cluster-based analyses

Primary analysis

Each cluster within current pollutant exposures

CUR6_BC_PAH6 – Black carbon (BC) and 6 PAHs
CUR6_PAH31 – a large cluster of 31 PAHs
CUR6_NkF – NkF only
CUR6_PM_RET – Particulate matter (PM) and retene
CUR6_NO2 – NO2 only
CUR6_SO2 – SO2 only

Summary the exposure estimates:

Characteristic	Overall, N = 1121	Smokeles, N = 171	Smoky, N = 871	Wood_and_or_Plant, N = 81
CUR6_BC_PAH6	0.79 (-0.5, 0.8)	-1.32 (-1.4, -0.9)	0.80 (-0.2, 1.1)	0.69 (0.1, 0.7)
(Missing)	3	2	1	0
CUR6_PAH31	0.38 (-0.4, 0.6)	-1.14 (-1.4, -0.5)	0.46 (-0.1, 0.6)	0.75 (0.4, 0.8)
(Missing)	3	2	1	0
CUR6_NkF	-0.40 (-0.6, 0.7)	0.06 (-0.2, 0.3)	-0.51 (-0.6, 0.9)	0.74 (-0.2, 0.7)
(Missing)	3	2	1	0
CUR6_PM_RET	-0.32 (-0.5, 0.4)	-0.04 (-0.9, 0.3)	-0.32 (-0.5, 0.1)	2.49 (0.9, 2.6)
(Missing)	3	2	1	0
CUR6_NO2	0.06 (-0.4, 0.8)	1.00 (0.6, 1.4)	-0.06 (-0.5, 0.5)	0.63 (-0.2, 1.3)
(Missing)	3	2	1	0
CUR6_SO2	-0.30 (-0.9, 0.3)	1.37 (0.2, 1.5)	-0.30 (-0.9, 0.1)	-1.00 (-1.3, -0.9)
(Missing)	3	2	1	0
1 Median (IQR)

In this section, we perform the generalized estimating equations (GEE) to evaluate the associations between each cluster within current pollutant exposures and each Epigenetic Age Acceleration with the formula: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * X \\ & + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations and X is one of the cluster estimates.

Results:

## The estimated effect of PAH31, PM_RET, and SO2:

## - PAH31:

##                                coefficient                std    ci_lower
## AgeAccelerationResidual        0.179918947  0.599591480222213 -0.99528035
## AgeAccelerationResidualHannum -0.217507058  0.531916739029904 -1.26006387
## AgeAccelPheno                  0.056370845  0.423703761684977 -0.77408853
## DNAmAgeSkinBloodClockAdjAge    0.328123856   0.40250386417309 -0.46078372
## AgeAccelGrim                   0.956799062  0.243092957560821  0.48033687
## DNAmTLAdjAge                  -0.009575373 0.0179803463468376 -0.04481685
## IEAA                           0.155478006   0.62280399099832 -1.06521782
## EEAA                          -0.314671432  0.655776990464098 -1.59999433
##                                 ci_upper                 p_val sig_level
## AgeAccelerationResidual       1.35511825     0.764124357900777    > 0.05
## AgeAccelerationResidualHannum 0.82504975     0.682604352090157    > 0.05
## AgeAccelPheno                 0.88683022     0.894159326536994    > 0.05
## DNAmAgeSkinBloodClockAdjAge   1.11703143     0.414953989697454    > 0.05
## AgeAccelGrim                  1.43326126 0.0000828720085680468  <= 0.001
## DNAmTLAdjAge                  0.02566611     0.594347444217059    > 0.05
## IEAA                          1.37617383     0.802864249566636    > 0.05
## EEAA                          0.97065147     0.631337479666618    > 0.05

## - PM_RET:

##                                coefficient                std    ci_lower
## AgeAccelerationResidual        0.282919687  0.542164666743684 -0.77972306
## AgeAccelerationResidualHannum -0.594253439  0.460423468427906 -1.49668344
## AgeAccelPheno                 -0.881989598  0.473846932830101 -1.81072959
## DNAmAgeSkinBloodClockAdjAge   -0.531613969  0.512932496910287 -1.53696166
## AgeAccelGrim                   0.711691043   0.40021179155664 -0.07272407
## DNAmTLAdjAge                   0.001229371 0.0248350912146731 -0.04744741
## IEAA                           0.474001722  0.478377520236743 -0.46361822
## EEAA                          -0.638737934  0.641835464350397 -1.89673544
##                                 ci_upper              p_val sig_level
## AgeAccelerationResidual       1.34556243  0.601786260868562    > 0.05
## AgeAccelerationResidualHannum 0.30817656  0.196819125638972    > 0.05
## AgeAccelPheno                 0.04675039 0.0626963719514023    > 0.05
## DNAmAgeSkinBloodClockAdjAge   0.47373373  0.300005816164569    > 0.05
## AgeAccelGrim                  1.49610615 0.0753568875564365    > 0.05
## DNAmTLAdjAge                  0.04990615  0.960519732149546    > 0.05
## IEAA                          1.41162166  0.321757445681559    > 0.05
## EEAA                          0.61925958  0.319651669252432    > 0.05

## - SO2:

##                                coefficient                std    ci_lower
## AgeAccelerationResidual       -0.286994938  0.543044412814683 -1.35136199
## AgeAccelerationResidualHannum -0.442858254  0.561931764100675 -1.54424451
## AgeAccelPheno                 -0.404952248  0.535295944166972 -1.45413230
## DNAmAgeSkinBloodClockAdjAge   -0.497489520  0.479474618041692 -1.43725977
## AgeAccelGrim                  -0.656941440  0.335913147267288 -1.31533121
## DNAmTLAdjAge                   0.002136417 0.0211895357252557 -0.03939507
## IEAA                          -0.538808790  0.497541485975896 -1.51399010
## EEAA                          -0.642248687  0.663793000788145 -1.94328297
##                                  ci_upper              p_val sig_level
## AgeAccelerationResidual       0.777372111  0.597157501311889    > 0.05
## AgeAccelerationResidualHannum 0.658528003  0.430638390917507    > 0.05
## AgeAccelPheno                 0.644227802  0.449348502282295    > 0.05
## DNAmAgeSkinBloodClockAdjAge   0.442280731  0.299469280072043    > 0.05
## AgeAccelGrim                  0.001448329 0.0505018741633129    > 0.05
## DNAmTLAdjAge                  0.043667907  0.919690048026774    > 0.05
## IEAA                          0.436372523  0.278833973504954    > 0.05
## EEAA                          0.658785595  0.333272343656849    > 0.05

b. Secondary analyses

i. Each cluster within cumulative lifetime pollutant exposures

Effect of each cluster for cumulative lifetime pollutant exposures and EAA-significant findings for PAH36 (PhenoAge, skin & blood) and NkF (GrimAge)

CUM6_BC_NO2_PM – a cluster of BC, NO2, and PM
CUM6_PAH36 – a large cluster of 36 PAHs
CUM6_DlP – DlP only
CUM6_NkF – NkF only
CUM6_RET – retene only
CUM6_SO2 – SO2 only

Summary the exposure estimates:

Characteristic	Overall, N = 1121	Smokeles, N = 171	Smoky, N = 871	Wood_and_or_Plant, N = 81
CUM6_BC_NO2_PM	0.22 (-0.6, 0.8)	0.19 (-0.3, 0.7)	0.10 (-1.0, 0.8)	1.38 (0.4, 1.6)
(Missing)	3	2	1	0
CUM6_PAH36	0.25 (-0.6, 1.1)	-1.00 (-1.2, -0.3)	0.32 (-0.5, 1.2)	0.83 (0.4, 1.4)
(Missing)	3	2	1	0
CUM6_DlP	-0.48 (-1.0, 0.8)	0.65 (0.5, 1.1)	-0.66 (-1.2, 0.7)	0.42 (0.3, 0.6)
(Missing)	3	2	1	0
CUM6_NkF	-0.22 (-0.8, 0.5)	-0.07 (-0.3, 0.4)	-0.31 (-1.0, 0.4)	1.18 (0.1, 1.7)
(Missing)	3	2	1	0
CUM6_RET	-0.22 (-0.7, 0.3)	-0.41 (-0.9, 0.3)	-0.25 (-0.8, 0.2)	1.71 (1.2, 1.9)
(Missing)	3	2	1	0
CUM6_SO2	0.09 (-0.4, 0.4)	1.13 (0.5, 1.6)	-0.03 (-0.9, 0.3)	-0.02 (-0.6, 0.1)
(Missing)	3	2	1	0
1 Median (IQR)

In this section, we perform the generalized estimating equations (GEE) to evaluate the associations between each cluster within current pollutant exposures and each Epigenetic Age Acceleration with the formula: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * X \\ & + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations and X is one of the cluster estimates.

Results:

## The estimated effect:

## $CUM6_BC_NO2_PM
##                               coefficient                std    ci_lower
## AgeAccelerationResidual        0.43919074  0.631148485831189 -0.79786029
## AgeAccelerationResidualHannum  0.80763378  0.583126631217737 -0.33529442
## AgeAccelPheno                  0.24037546  0.597112512729987 -0.92996506
## DNAmAgeSkinBloodClockAdjAge    0.02903798  0.643996566831562 -1.23319529
## AgeAccelGrim                   0.88584155  0.381991449740532  0.13713831
## DNAmTLAdjAge                  -0.02617337 0.0204665722907726 -0.06628786
## IEAA                           0.51797290  0.622382761835361 -0.70189731
## EEAA                           1.21145760  0.719983109819847 -0.19970929
##                                 ci_upper              p_val sig_level
## AgeAccelerationResidual       1.67624177  0.486516751453832    > 0.05
## AgeAccelerationResidualHannum 1.95056197   0.16605068840787    > 0.05
## AgeAccelPheno                 1.41071599  0.687269662680335    > 0.05
## DNAmAgeSkinBloodClockAdjAge   1.29127125  0.964035351565247    > 0.05
## AgeAccelGrim                  1.63454479  0.020394556474622   <= 0.05
## DNAmTLAdjAge                  0.01394111  0.200955081865264    > 0.05
## IEAA                          1.73784312  0.405272515460532    > 0.05
## EEAA                          2.62262450 0.0924487785151595    > 0.05
## 
## $CUM6_PAH36
##                               coefficient                std    ci_lower
## AgeAccelerationResidual        0.32098720  0.611925713323755 -0.87838720
## AgeAccelerationResidualHannum  0.79799243  0.550924632652803 -0.28181985
## AgeAccelPheno                  0.88347270   0.49081691235789 -0.07852845
## DNAmAgeSkinBloodClockAdjAge    0.68273150  0.467643833931782 -0.23385042
## AgeAccelGrim                   1.26782692  0.266136683806017  0.74619902
## DNAmTLAdjAge                  -0.01778507 0.0224287041031589 -0.06174533
## IEAA                           0.12890744   0.58213223960231 -1.01207175
## EEAA                           1.10979634  0.657537766874319 -0.17897769
##                                 ci_upper                  p_val sig_level
## AgeAccelerationResidual       1.52036160      0.599894269204706    > 0.05
## AgeAccelerationResidualHannum 1.87780471      0.147488376392156    > 0.05
## AgeAccelPheno                 1.84547385     0.0718599112701807    > 0.05
## DNAmAgeSkinBloodClockAdjAge   1.59931341      0.144306824237202    > 0.05
## AgeAccelGrim                  1.78945482 0.00000189963151164818  <= 0.001
## DNAmTLAdjAge                  0.02617519      0.427800934933145    > 0.05
## IEAA                          1.26988663      0.824749749715961    > 0.05
## EEAA                          2.39857036     0.0914484321808867    > 0.05
## 
## $CUM6_DlP
##                               coefficient                std    ci_lower
## AgeAccelerationResidual        0.77555748     0.538282539327 -0.27947630
## AgeAccelerationResidualHannum  0.13266342  0.547499804834951 -0.94043620
## AgeAccelPheno                  0.13137771  0.423715560332615 -0.69910479
## DNAmAgeSkinBloodClockAdjAge    0.15937935   0.43754145074402 -0.69820189
## AgeAccelGrim                  -0.51862899  0.250819643345407 -1.01023550
## DNAmTLAdjAge                  -0.03317558 0.0203889054457161 -0.07313783
## IEAA                           0.79059038  0.478660990765329 -0.14758516
## EEAA                           0.32622647  0.681509729501011 -1.00953260
##                                   ci_upper              p_val sig_level
## AgeAccelerationResidual        1.830591252  0.149641197410507    > 0.05
## AgeAccelerationResidualHannum  1.205763040  0.808541742146501    > 0.05
## AgeAccelPheno                  0.961860204  0.756514506007147    > 0.05
## DNAmAgeSkinBloodClockAdjAge    1.016960597  0.715663004799903    > 0.05
## AgeAccelGrim                  -0.027022494 0.0386647839033525   <= 0.05
## DNAmTLAdjAge                   0.006786677  0.103707641590583    > 0.05
## IEAA                           1.728765921 0.0986016793596494    > 0.05
## EEAA                           1.661985540  0.632164841806952    > 0.05
## 
## $CUM6_NkF
##                               coefficient                std    ci_lower
## AgeAccelerationResidual        0.23487496  0.370044825728772 -0.49041290
## AgeAccelerationResidualHannum  0.18253721  0.384715243765542 -0.57150467
## AgeAccelPheno                 -0.13755590  0.400990085688409 -0.92349647
## DNAmAgeSkinBloodClockAdjAge   -0.13582133  0.370451346197561 -0.86190596
## AgeAccelGrim                   0.64420288  0.234144979073553  0.18527872
## DNAmTLAdjAge                  -0.03856632 0.0178706277241984 -0.07359275
## IEAA                           0.10120983  0.322496477050339 -0.53088327
## EEAA                           0.36223969  0.506026915398441 -0.62957306
##                                   ci_upper              p_val sig_level
## AgeAccelerationResidual        0.960162816  0.525610861031605    > 0.05
## AgeAccelerationResidualHannum  0.936579089  0.635162247528876    > 0.05
## AgeAccelPheno                  0.648384668  0.731567879106851    > 0.05
## DNAmAgeSkinBloodClockAdjAge    0.590263312  0.713889491109655    > 0.05
## AgeAccelGrim                   1.103127039 0.0059359409455727   <= 0.01
## DNAmTLAdjAge                  -0.003539885 0.0309213024151503   <= 0.05
## IEAA                           0.733302924  0.753648369225893    > 0.05
## EEAA                           1.354052449  0.474083575442915    > 0.05
## 
## $CUM6_RET
##                               coefficient                std    ci_lower
## AgeAccelerationResidual        0.28350619  0.552776084605805 -0.79993493
## AgeAccelerationResidualHannum -0.04385919  0.439316104162012 -0.90491875
## AgeAccelPheno                 -0.45574854  0.456416857488748 -1.35032558
## DNAmAgeSkinBloodClockAdjAge   -0.35395161  0.506828431781252 -1.34733533
## AgeAccelGrim                   0.77897390  0.367619500826848  0.05843968
## DNAmTLAdjAge                  -0.01093498 0.0214052866794078 -0.05288934
## IEAA                           0.46029630  0.481308743347077 -0.48306884
## EEAA                           0.09516821  0.593702260027804 -1.06848822
##                                 ci_upper             p_val sig_level
## AgeAccelerationResidual       1.36694732 0.608037294034898    > 0.05
## AgeAccelerationResidualHannum 0.81720038 0.920475208898362    > 0.05
## AgeAccelPheno                 0.43882850 0.318019646128447    > 0.05
## DNAmAgeSkinBloodClockAdjAge   0.63943212  0.48494850133759    > 0.05
## AgeAccelGrim                  1.49950812  0.03409318414692   <= 0.05
## DNAmTLAdjAge                  0.03101938 0.609453120106078    > 0.05
## IEAA                          1.40366143 0.338898920957422    > 0.05
## EEAA                          1.25882464 0.872647766059968    > 0.05
## 
## $CUM6_SO2
##                               coefficient                std    ci_lower
## AgeAccelerationResidual       -0.18136654  0.663187852506667 -1.48121473
## AgeAccelerationResidualHannum  0.03674512  0.642184502653674 -1.22193651
## AgeAccelPheno                  0.22680218  0.512304050348761 -0.77731376
## DNAmAgeSkinBloodClockAdjAge   -0.12025374  0.548771843574112 -1.19584655
## AgeAccelGrim                  -0.32759841   0.33232290087991 -0.97895130
## DNAmTLAdjAge                   0.02036273 0.0236714040514353 -0.02603323
## IEAA                          -0.05044750  0.573209621038454 -1.17393836
## EEAA                          -0.26011910  0.758709096644643 -1.74718893
##                                 ci_upper             p_val sig_level
## AgeAccelerationResidual       1.11848165  0.78448667028792    > 0.05
## AgeAccelerationResidualHannum 1.29542674  0.95437079268678    > 0.05
## AgeAccelPheno                 1.23091812 0.657975431702584    > 0.05
## DNAmAgeSkinBloodClockAdjAge   0.95533908 0.826546848623498    > 0.05
## AgeAccelGrim                  0.32375447 0.324239392000058    > 0.05
## DNAmTLAdjAge                  0.06675868 0.389665196116368    > 0.05
## IEAA                          1.07304336 0.929869668259585    > 0.05
## EEAA                          1.22695073 0.731715586060948    > 0.05

ii. Each cluster within childhood pollutant exposures

Effect of each cluster with childhood pollutant exposures and EAA-significant LRT P value for GrimAge but no findings for linear regression

Summary the exposure estimates:

Characteristic	Overall, N = 1121	Smokeles, N = 171	Smoky, N = 871	Wood_and_or_Plant, N = 81
CHLD5_X7	0.09 (-0.5, 0.5)	-0.63 (-0.9, -0.1)	0.10 (-0.5, 0.3)	0.86 (0.7, 1.1)
(Missing)	3	2	1	0
CHLD5_X33	0.23 (-0.7, 1.1)	-0.83 (-1.4, 0.1)	0.51 (-0.4, 1.2)	0.95 (-0.1, 1.0)
(Missing)	3	2	1	0
CHLD5_NkF	-0.21 (-0.8, 0.7)	0.06 (-0.3, 0.7)	-0.45 (-1.0, 0.5)	1.07 (0.5, 1.5)
(Missing)	3	2	1	0
CHLD5_NO2	0.34 (-0.5, 0.8)	0.17 (-0.5, 0.9)	0.43 (-0.6, 0.8)	-0.21 (-0.3, 0.2)
(Missing)	3	2	1	0
CHLD5_SO2	0.34 (-0.7, 0.4)	0.45 (0.3, 1.4)	0.34 (-0.9, 0.4)	0.22 (-0.2, 0.3)
(Missing)	3	2	1	0
1 Median (IQR)

In this section, we perform the generalized estimating equations (GEE) to evaluate the associations between each cluster within current pollutant exposures and each Epigenetic Age Acceleration with the formula: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * X \\ & + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations and X is one of the cluster estimates.

Results:

## The estimated effect:

## $CHLD5_X7
##                                coefficient                std    ci_lower
## AgeAccelerationResidual        0.391660140  0.618740975533831 -0.82107217
## AgeAccelerationResidualHannum  0.721337116  0.553205104763011 -0.36294489
## AgeAccelPheno                  0.371667874  0.462674392636695 -0.53517394
## DNAmAgeSkinBloodClockAdjAge    0.236313225  0.513186722671679 -0.76953275
## AgeAccelGrim                   0.846748104  0.281735272354069  0.29454697
## DNAmTLAdjAge                  -0.004458368 0.0222524698597903 -0.04807321
## IEAA                           0.232192431  0.568035210839522 -0.88115658
## EEAA                           1.134705151  0.678491104237556 -0.19513741
##                                 ci_upper               p_val sig_level
## AgeAccelerationResidual       1.60439245   0.526736701432907    > 0.05
## AgeAccelerationResidualHannum 1.80561912    0.19225967970952    > 0.05
## AgeAccelPheno                 1.27850968   0.421799441579741    > 0.05
## DNAmAgeSkinBloodClockAdjAge   1.24215920   0.645170322036037    > 0.05
## AgeAccelGrim                  1.39894924 0.00265167053617776   <= 0.01
## DNAmTLAdjAge                  0.03915647   0.841203863644298    > 0.05
## IEAA                          1.34554144   0.682712749838417    > 0.05
## EEAA                          2.46454771  0.0944464515125873    > 0.05
## 
## $CHLD5_X33
##                                coefficient                std     ci_lower
## AgeAccelerationResidual        0.125946325   0.52239966247448 -0.897957013
## AgeAccelerationResidualHannum  0.846452786  0.512987995088683 -0.159003684
## AgeAccelPheno                  1.173721616  0.419669531582024  0.351169334
## DNAmAgeSkinBloodClockAdjAge    0.820091780  0.406034251795275  0.024264646
## AgeAccelGrim                   0.985616343  0.286559700801614  0.423959330
## DNAmTLAdjAge                  -0.002651323 0.0198442252994671 -0.041546004
## IEAA                          -0.368518591  0.510493457854136 -1.369085768
## EEAA                           1.198477000  0.614506643979418 -0.005956023
##                                 ci_upper                p_val sig_level
## AgeAccelerationResidual       1.14984966     0.80948389358743    > 0.05
## AgeAccelerationResidualHannum 1.85190926   0.0989339278911389    > 0.05
## AgeAccelPheno                 1.99627390  0.00516153267568586   <= 0.01
## DNAmAgeSkinBloodClockAdjAge   1.61591891    0.043408278084326   <= 0.05
## AgeAccelGrim                  1.54727336 0.000582832970125735  <= 0.001
## DNAmTLAdjAge                  0.03624336    0.893713530970616    > 0.05
## IEAA                          0.63204859    0.470363954269536    > 0.05
## EEAA                          2.40291002   0.0511394631926851    > 0.05

4. 5 methylchrysene (5MC)

Summary the exposure estimates:

Characteristic	Overall, N = 1121	Smokeles, N = 171	Smoky, N = 871	Wood_and_or_Plant, N = 81
cur_5mc	7.81 (5.2, 9.7)	2.59 (2.2, 4.0)	9.46 (5.6, 10.1)	7.40 (7.1, 7.4)
(Missing)	3	2	1	0
cum_5mc	253.00 (157.7, 371.9)	92.43 (82.6, 167.9)	270.50 (179.9, 389.1)	341.46 (228.8, 471.5)
(Missing)	3	2	1	0
bir_5mc	4.83 (2.6, 8.2)	2.10 (1.5, 4.4)	4.83 (3.0, 8.2)	8.02 (3.7, 8.8)
(Missing)	3	2	1	0
cur_5mc_measured	5.64 (2.1, 8.7)	1.82 (1.0, 2.3)	7.02 (4.4, 9.7)	4.64 (1.6, 6.8)
(Missing)	67	10	55	2
1 Median (IQR)

## [1] "Pearson pair-wise correlation:"

##                    cur_5mc   cum_5mc     bir_5mc cur_5mc_measured
## cur_5mc          1.0000000 0.7111247  0.66703068       0.12831853
## cum_5mc          0.7111247 1.0000000  0.81831424       0.18850136
## bir_5mc          0.6670307 0.8183142  1.00000000      -0.02596987
## cur_5mc_measured 0.1283185 0.1885014 -0.02596987       1.00000000

## [1] "Spearman pair-wise correlation:"

##                    cur_5mc   cum_5mc   bir_5mc cur_5mc_measured
## cur_5mc          1.0000000 0.6758096 0.6142748        0.4631276
## cum_5mc          0.6758096 1.0000000 0.7985763        0.3433940
## bir_5mc          0.6142748 0.7985763 1.0000000        0.3065790
## cur_5mc_measured 0.4631276 0.3433940 0.3065790        1.0000000

Primary analyses

Effect of modeled current exposure to 5MC and EAA

Generalized estimating equations (GEE)

No confounders

In the following section, we performed generalized estimating equations (GEE) with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 *cur\_5mc + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations.

The estimations of \(\beta_1\) with given \(Y\) and current \(5MC\) are shown below, which can be interpreted as “the mean of Y changes given a one-unit increase in current 5MC while holding other variables constant”.

## [1] " Result data: "

##                                coefficient                 std    ci_lower
## AgeAccelerationResidual        0.019159838   0.119620299681397 -0.21529595
## AgeAccelerationResidualHannum -0.041572743    0.10121495306854 -0.23995405
## AgeAccelPheno                  0.063948683   0.100238231551026 -0.13251825
## DNAmAgeSkinBloodClockAdjAge    0.104833952   0.075228712841312 -0.04261433
## AgeAccelGrim                   0.149523037  0.0565650873465267  0.03865547
## DNAmTLAdjAge                  -0.003814336 0.00353079663956072 -0.01073470
## IEAA                           0.001582042   0.127834093339104 -0.24897278
## EEAA                          -0.053365322   0.132090247562408 -0.31226221
##                                  ci_upper               p_val sig_level
## AgeAccelerationResidual       0.253615625   0.872745484941928    > 0.05
## AgeAccelerationResidualHannum 0.156808565   0.681265269321088    > 0.05
## AgeAccelPheno                 0.260415616   0.523495170158012    > 0.05
## DNAmAgeSkinBloodClockAdjAge   0.252282229   0.163457636388872    > 0.05
## AgeAccelGrim                  0.260390608 0.00820827841291805   <= 0.01
## DNAmTLAdjAge                  0.003106025   0.280006515297229    > 0.05
## IEAA                          0.252136865   0.990125837758072    > 0.05
## EEAA                          0.205531563   0.686207920603063    > 0.05
##                                         EAAs
## AgeAccelerationResidual          Horvath EAA
## AgeAccelerationResidualHannum     Hannum EAA
## AgeAccelPheno                   PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge   Skin&Blood EAA
## AgeAccelGrim                     GrimAge EAA
## DNAmTLAdjAge                          DNAmTL
## IEAA                                    IEAA
## EEAA                                    EEAA

Mixed model

In the following section, we performed generalized estimating equations (GEE) with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 *cur\_5mc\\ & + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations.

The estimations of \(\beta_1\) with given \(Y\) and current \(5MC\) are shown below, which can be interpreted as “the mean of Y changes given a one-unit increase in current 5MC while holding other variables constant”.

## [1] " Result data: "

##                                coefficient                 std    ci_lower
## AgeAccelerationResidual        0.015878690   0.123212614165765 -0.22561803
## AgeAccelerationResidualHannum -0.051171412   0.103935109316447 -0.25488423
## AgeAccelPheno                  0.091359722  0.0974953712583224 -0.09973121
## DNAmAgeSkinBloodClockAdjAge    0.114010349  0.0766727133240309 -0.03626817
## AgeAccelGrim                   0.167727453  0.0513461606401706  0.06708898
## DNAmTLAdjAge                  -0.004550865 0.00347496993471429 -0.01136181
## IEAA                           0.003400224    0.13227439652008 -0.25585759
## EEAA                          -0.068201795    0.13857957225156 -0.33981776
##                                  ci_upper               p_val sig_level
## AgeAccelerationResidual       0.257375413   0.897458716697316    > 0.05
## AgeAccelerationResidualHannum 0.152541402   0.622479003468804    > 0.05
## AgeAccelPheno                 0.282450650   0.348723953264527    > 0.05
## DNAmAgeSkinBloodClockAdjAge   0.264288867   0.137021616586443    > 0.05
## AgeAccelGrim                  0.268365928 0.00108846725724532   <= 0.01
## DNAmTLAdjAge                  0.002260076   0.190326866190183    > 0.05
## IEAA                          0.262658041   0.979491967938557    > 0.05
## EEAA                          0.203414167   0.622614029655613    > 0.05
##                                         EAAs
## AgeAccelerationResidual          Horvath EAA
## AgeAccelerationResidualHannum     Hannum EAA
## AgeAccelPheno                   PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge   Skin&Blood EAA
## AgeAccelGrim                     GrimAge EAA
## DNAmTLAdjAge                          DNAmTL
## IEAA                                    IEAA
## EEAA                                    EEAA

Linear Regression (mix model)

In the following section, we performed linear regression with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 *cur\_5mc\\ & + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations.

The estimations of \(\beta_1\) with given \(Y\) and \(5MC\) are shown below, which can be interpreted as “the mean of Y changes given a one-unit increase in current 5MC while holding other variables constant”.

Including all visits

## [1] " Result data: "

##                               coefficient                  std    ci_lower
## AgeAccelerationResidual                NA   0.0207336699605963 -0.18496178
## AgeAccelerationResidualHannum          NA  -0.0418870573708888 -0.22076438
## AgeAccelPheno                          NA    0.100522348290013 -0.07676839
## DNAmAgeSkinBloodClockAdjAge            NA    0.110087986451079 -0.04343307
## AgeAccelGrim                           NA    0.185902269475276  0.07207875
## DNAmTLAdjAge                           NA -0.00304693593610302 -0.01092097
## IEAA                                   NA   0.0104750365496712 -0.17787726
## EEAA                                   NA  -0.0454429780379584 -0.27130842
##                                  ci_upper               p_val sig_level
## AgeAccelerationResidual       0.226429117   0.843750902791681    > 0.05
## AgeAccelerationResidualHannum 0.136990268   0.647164122161031    > 0.05
## AgeAccelPheno                 0.277813088   0.268860884675762    > 0.05
## DNAmAgeSkinBloodClockAdjAge   0.263609043   0.162693464549318    > 0.05
## AgeAccelGrim                  0.299725793 0.00178954419368209   <= 0.01
## DNAmTLAdjAge                  0.004827099   0.449808711450213    > 0.05
## IEAA                          0.198827332   0.913398260459483    > 0.05
## EEAA                          0.180422468    0.69409202484541    > 0.05
##                                         EAAs
## AgeAccelerationResidual          Horvath EAA
## AgeAccelerationResidualHannum     Hannum EAA
## AgeAccelPheno                   PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge   Skin&Blood EAA
## AgeAccelGrim                     GrimAge EAA
## DNAmTLAdjAge                          DNAmTL
## IEAA                                    IEAA
## EEAA                                    EEAA

Only including one of the two visits

Including all of the single visits and the 1st visit for these 23 subjects who have been visited twice.

Including all of the single visits and the 2nd visit for these 23 subjects who have been visited twice.

Secondary analyses

i. Effect of modeled cumulative exposure to 5MC and EAA

Generalized estimating equations (GEE)

In the following section, we performed generalized estimating equations (GEE) with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 *cum\_5mc\\ & + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations.

The estimations of \(\beta_1\) with given \(Y\) and cumulative \(5MC\) are shown below, which can be interpreted as “the mean of Y changes given a one-unit increase in cumulative 5MC while holding other variables constant”.

## [1] " Result data: "

##                                 coefficient                  std      ci_lower
## AgeAccelerationResidual        0.0002634684  0.00356364056862806 -0.0067212672
## AgeAccelerationResidualHannum  0.0034941270  0.00303739789484009 -0.0024591729
## AgeAccelPheno                  0.0044586214  0.00306589829668175 -0.0015505393
## DNAmAgeSkinBloodClockAdjAge    0.0025381617  0.00268283943639575 -0.0027202036
## AgeAccelGrim                   0.0059797751  0.00174133579802403  0.0025667569
## DNAmTLAdjAge                  -0.0001321556 0.000141285261175084 -0.0004090747
## IEAA                          -0.0019359695     0.00335384774017 -0.0085095111
## EEAA                           0.0054840443  0.00389919260451722 -0.0021583732
##                                   ci_upper                p_val sig_level
## AgeAccelerationResidual       0.0072482039    0.941064206728588    > 0.05
## AgeAccelerationResidualHannum 0.0094474269    0.249992106478145    > 0.05
## AgeAccelPheno                 0.0104677820    0.145873502832328    > 0.05
## DNAmAgeSkinBloodClockAdjAge   0.0077965270    0.344111409021191    > 0.05
## AgeAccelGrim                  0.0093927932 0.000594708988064241  <= 0.001
## DNAmTLAdjAge                  0.0001447635    0.349591731827019    > 0.05
## IEAA                          0.0046375721    0.563778458819213    > 0.05
## EEAA                          0.0131264618    0.159588653069498    > 0.05
##                                         EAAs
## AgeAccelerationResidual          Horvath EAA
## AgeAccelerationResidualHannum     Hannum EAA
## AgeAccelPheno                   PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge   Skin&Blood EAA
## AgeAccelGrim                     GrimAge EAA
## DNAmTLAdjAge                          DNAmTL
## IEAA                                    IEAA
## EEAA                                    EEAA

Linear Regression (mix model)

In the following section, we performed linear regression with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 *cum\_5mc\\ & + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations.

The estimations of \(\beta_1\) with given \(Y\) and \(5MC\) are shown below, which can be interpreted as “the mean of Y changes given a one-unit increase in cumulative 5MC while holding other variables constant”.

Including all visits

## [1] " Result data: "

##                               coefficient                    std      ci_lower
## AgeAccelerationResidual                NA    0.00103650747662543 -0.0053403710
## AgeAccelerationResidualHannum          NA    0.00379397141765368 -0.0017129311
## AgeAccelPheno                          NA    0.00456487265964867 -0.0008975400
## DNAmAgeSkinBloodClockAdjAge            NA     0.0027605243832414 -0.0020149908
## AgeAccelGrim                           NA    0.00637247485005719  0.0028794399
## DNAmTLAdjAge                           NA -0.0000862320291587002 -0.0003305153
## IEAA                                   NA    -0.0010312827043171 -0.0068692888
## EEAA                                   NA    0.00581872108184905 -0.0011055319
##                                   ci_upper               p_val sig_level
## AgeAccelerationResidual       0.0074133860   0.750648090319113    > 0.05
## AgeAccelerationResidualHannum 0.0093008739   0.179679063556041    > 0.05
## AgeAccelPheno                 0.0100272853   0.104289937607713    > 0.05
## DNAmAgeSkinBloodClockAdjAge   0.0075360396   0.259680451090652    > 0.05
## AgeAccelGrim                  0.0098655098 0.00052036941541486  <= 0.001
## DNAmTLAdjAge                  0.0001580512    0.49047028014843    > 0.05
## IEAA                          0.0048067234    0.72982851067324    > 0.05
## EEAA                          0.0127429741   0.102398656798027    > 0.05
##                                         EAAs
## AgeAccelerationResidual          Horvath EAA
## AgeAccelerationResidualHannum     Hannum EAA
## AgeAccelPheno                   PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge   Skin&Blood EAA
## AgeAccelGrim                     GrimAge EAA
## DNAmTLAdjAge                          DNAmTL
## IEAA                                    IEAA
## EEAA                                    EEAA

ii. Effect of modeled birth home/childhood exposure to 5MC

Generalized estimating equations (GEE)

In the following section, we performed generalized estimating equations (GEE) with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 *birth\_5mc\\ & + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations.

The estimations of \(\beta_1\) with given \(Y\) and birth home/childhood \(5MC\) are shown below, which can be interpreted as “the mean of Y changes given a one-unit increase in birth home/childhood 5MC while holding other variables constant”.

## [1] " Result data: "

##                                coefficient                 std    ci_lower
## AgeAccelerationResidual        0.002549647   0.175648408840649 -0.34172123
## AgeAccelerationResidualHannum  0.214124879   0.156901216094478 -0.09340150
## AgeAccelPheno                  0.328090148    0.15503211923717  0.02422719
## DNAmAgeSkinBloodClockAdjAge    0.184739910   0.127394623047048 -0.06495355
## AgeAccelGrim                   0.316489482  0.0963285908716984  0.12768544
## DNAmTLAdjAge                  -0.003382397 0.00704699006180516 -0.01719450
## IEAA                          -0.172753745   0.164532364342251 -0.49523718
## EEAA                           0.313411448   0.201414432212561 -0.08136084
##                                ci_upper               p_val sig_level
## AgeAccelerationResidual       0.3468205   0.988418609834732    > 0.05
## AgeAccelerationResidualHannum 0.5216513   0.172343774292717    > 0.05
## AgeAccelPheno                 0.6319531  0.0343216724056407   <= 0.05
## DNAmAgeSkinBloodClockAdjAge   0.4344334   0.147019764983751    > 0.05
## AgeAccelGrim                  0.5052935 0.00101794428051305   <= 0.01
## DNAmTLAdjAge                  0.0104297   0.631243324757604    > 0.05
## IEAA                          0.1497297   0.293732747964983    > 0.05
## EEAA                          0.7081837   0.119695588251105    > 0.05
##                                         EAAs
## AgeAccelerationResidual          Horvath EAA
## AgeAccelerationResidualHannum     Hannum EAA
## AgeAccelPheno                   PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge   Skin&Blood EAA
## AgeAccelGrim                     GrimAge EAA
## DNAmTLAdjAge                          DNAmTL
## IEAA                                    IEAA
## EEAA                                    EEAA

Linear Regression (mix model)

In the following section, we performed linear regression with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 *birth\_5mc\\ & + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations.

The estimations of \(\beta_1\) with given \(Y\) and birth home/childhood \(5MC\) are shown below, which can be interpreted as “the mean of Y changes given a one-unit increase in birth home/childhood 5MC while holding other variables constant”.

Including all visits

## [1] " Result data: "

##                               coefficient                  std    ci_lower
## AgeAccelerationResidual                NA   0.0097828988641446 -0.31111335
## AgeAccelerationResidualHannum          NA    0.206939438405666 -0.06965063
## AgeAccelPheno                          NA    0.323913401197371  0.05249473
## DNAmAgeSkinBloodClockAdjAge            NA    0.197237399792455 -0.04153770
## AgeAccelGrim                           NA    0.316458874438049  0.14050344
## DNAmTLAdjAge                           NA -0.00113667204603363 -0.01344886
## IEAA                                   NA   -0.142531806403035 -0.43513042
## EEAA                                   NA    0.305657343480753 -0.04223925
##                                 ci_upper                p_val sig_level
## AgeAccelerationResidual       0.33067915    0.952460860656415    > 0.05
## AgeAccelerationResidualHannum 0.48352950    0.145382879129861    > 0.05
## AgeAccelPheno                 0.59533207    0.021136962042337   <= 0.05
## DNAmAgeSkinBloodClockAdjAge   0.43601249    0.108303371907858    > 0.05
## AgeAccelGrim                  0.49241430 0.000618237725277051  <= 0.001
## DNAmTLAdjAge                  0.01117552    0.856740648508633    > 0.05
## IEAA                          0.15006680    0.341791023208332    > 0.05
## EEAA                          0.65355394   0.0878748036123461    > 0.05
##                                         EAAs
## AgeAccelerationResidual          Horvath EAA
## AgeAccelerationResidualHannum     Hannum EAA
## AgeAccelPheno                   PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge   Skin&Blood EAA
## AgeAccelGrim                     GrimAge EAA
## DNAmTLAdjAge                          DNAmTL
## IEAA                                    IEAA
## EEAA                                    EEAA

iii. Effect of measured current exposure to 5MC

Generalized estimating equations (GEE)

In the following section, we performed generalized estimating equations (GEE) with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 *cur\_measured\_5mc\\ & + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations.

The estimations of \(\beta_1\) with given \(Y\) and measured current exposure to \(5MC\) are shown below, which can be interpreted as “the mean of Y changes given a one-unit increase in measured current exposure to 5MC while holding other variables constant”.

## [1] " Result data: "

##                                 coefficient                  std      ci_lower
## AgeAccelerationResidual       -0.0160930304   0.0116088132181777 -0.0388463043
## AgeAccelerationResidualHannum  0.0121388435  0.00937371426180123 -0.0062336364
## AgeAccelPheno                 -0.0124219804   0.0105810182674426 -0.0331607762
## DNAmAgeSkinBloodClockAdjAge    0.0052270379   0.0103531894559643 -0.0150652135
## AgeAccelGrim                   0.0096229772   0.0070919616377069 -0.0042772676
## DNAmTLAdjAge                  -0.0002604479 0.000327670681452122 -0.0009026825
## IEAA                          -0.0222372500  0.00822552175149344 -0.0383592726
## EEAA                           0.0153629305   0.0111540005231729 -0.0064989105
##                                    ci_upper               p_val sig_level
## AgeAccelerationResidual        0.0066602435   0.165662347120224    > 0.05
## AgeAccelerationResidualHannum  0.0305113235   0.195324515612494    > 0.05
## AgeAccelPheno                  0.0083168154   0.240400138182616    > 0.05
## DNAmAgeSkinBloodClockAdjAge    0.0255192892   0.613648578514261    > 0.05
## AgeAccelGrim                   0.0235232220   0.174817716392433    > 0.05
## DNAmTLAdjAge                   0.0003817866   0.426702704133811    > 0.05
## IEAA                          -0.0061152274 0.00686246950979708   <= 0.01
## EEAA                           0.0372247716   0.168404952099984    > 0.05
##                                         EAAs
## AgeAccelerationResidual          Horvath EAA
## AgeAccelerationResidualHannum     Hannum EAA
## AgeAccelPheno                   PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge   Skin&Blood EAA
## AgeAccelGrim                     GrimAge EAA
## DNAmTLAdjAge                          DNAmTL
## IEAA                                    IEAA
## EEAA                                    EEAA

Linear Regression (mix model)

In the following section, we performed linear regression with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 *cum\_5mc\\ & + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations.

The estimations of \(\beta_1\) with given \(Y\) and measured current exposure to 5MC$ are shown below, which can be interpreted as “the mean of Y changes given a one-unit increase in measured current exposure to 5MC while holding other variables constant”.

Including all visits

## [1] " Result data: "

##                               coefficient                   std     ci_lower
## AgeAccelerationResidual                NA   -0.0160227069180148 -0.054216478
## AgeAccelerationResidualHannum          NA    0.0121700572336292 -0.019861322
## AgeAccelPheno                          NA   -0.0121558094003618 -0.046147335
## DNAmAgeSkinBloodClockAdjAge            NA   0.00531057870964733 -0.026971127
## AgeAccelGrim                           NA   0.00965996767859186 -0.013302592
## DNAmTLAdjAge                           NA -0.000258983966760264 -0.001783975
## IEAA                                   NA    -0.022160761584899 -0.054881608
## EEAA                                   NA    0.0153953785867021 -0.024871166
##                                  ci_upper             p_val sig_level
## AgeAccelerationResidual       0.022171064 0.415583167756025    > 0.05
## AgeAccelerationResidualHannum 0.044201436   0.4606084575144    > 0.05
## AgeAccelPheno                 0.021835716 0.487217454828268    > 0.05
## DNAmAgeSkinBloodClockAdjAge   0.037592285  0.74872318878492    > 0.05
## AgeAccelGrim                  0.032622528 0.414291462572805    > 0.05
## DNAmTLAdjAge                  0.001266007 0.740895770814662    > 0.05
## IEAA                          0.010560085 0.191532466581911    > 0.05
## EEAA                          0.055661923 0.457805164498975    > 0.05
##                                         EAAs
## AgeAccelerationResidual          Horvath EAA
## AgeAccelerationResidualHannum     Hannum EAA
## AgeAccelPheno                   PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge   Skin&Blood EAA
## AgeAccelGrim                     GrimAge EAA
## DNAmTLAdjAge                          DNAmTL
## IEAA                                    IEAA
## EEAA                                    EEAA

5. Additional secondary analyses